Evidence for Gravitational Lensing in FRB 20190320B: A Potential Lens Mass of ∼420 Solar Masses
Bibliographic record
Abstract
Abstract Due to their millisecond-duration pulses and high flux, fast radio bursts (FRBs) are ideal probes for detecting low-mass gravitational lensing signals, as their distinctive characteristics make them highly sensitive to subtle time delays and lensing effects. We search for lensing events using the high-time-resolution (2.56 μ s) channelized raw voltage (baseband) data of 58 FRBs consisting of at least two components publicly released by the Canadian Hydrogen Intensity Mapping Experiment/FRB Collaboration, which allows the microstructures of FRBs to be better resolved compared to the intensity data. We apply different algorithms to examine these FRBs and identify a lensing candidate, FRB 20190320B, with a delay of ∼1.24 ms and an intensity ratio of ∼1.16 between the two pulses. Although some methods show a significance exceeding 5 σ , we conservatively regard this event as a candidate due to the potential limitations of these methods. Assuming it is indeed a lensed signal, we estimate its redshifted lens mass to be ∼424 M ⊙ , which ranks among the lowest mass estimates reported for candidate lensed FRBs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".